在内核中引入机器学习框架，实现自适应配置优化。
Machine Learning (ML) library in Linux kernel
- 设计轻量级内核ML架构，支持用户态模型与内核态代理交互。
- 通过概念验证项目证明内核运行机器学习模型的可行性。
- 解决无浮点运算支持和性能下降难题，适合系统级优化研究者。
Linux内核代码庞大、子系统繁多且配置选项极多，导致配置效率低下。机器学习能从数据中发现规律并做出预测，赋予内核自我演化能力。但将机器学习引入内核面临挑战：内核空间不支持浮点运算（FPU），且模型可能引发显著性能下降。本文提出一种内核级机器学习基础设施架构，旨在解决上述问题，并实现内核空间中机器学习模型的部署。所提出的内核ML库已作为概念验证（PoC）项目实现，展示了内核态模型代理与用户态线程间交互接口的设计与可行性，验证了在内核中使用机器学习模型的潜在路径。
原文摘要 · Abstract (English)
Linux kernel is a huge code base with enormous number of subsystems and possible configuration options that results in unmanageable complexity of elaborating an efficient configuration. Machine Learning (ML) is approach/area of learning from data, finding patterns, and making predictions without implementing algorithms by developers that can introduce a self-evolving capability in Linux kernel. However, introduction of ML approaches in Linux kernel is not easy way because there is no direct use of floating-point operations (FPU) in kernel space and, potentially, ML models can be a reason of significant performance degradation in Linux kernel. Paper suggests the ML infrastructure architecture in Linux kernel that can solve the declared problem and introduce of employing ML models in kernel space. Suggested approach of kernel ML library has been implemented as Proof Of Concept (PoC) project with the goal to demonstrate feasibility of the suggestion and to design the interface of interaction the kernel-space ML model proxy and the ML model user-space thread.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。